The efficient identification of rice pests and diseases is crucial for preventing crop damage. To address the limitations of traditional manual detection methods and machine learning-based approaches, a new rice pest and disease recognition model based on an improved YOLOv7 algorithm has been developed. The model utilizes the lightweight network MobileNetV3 for feature extraction, reducing parameterization, and incorporates the coordinate attention mechanism (CA) and the SIoU loss function for enhanced accuracy. The model has been tested on a dataset of 3773 rice pest and disease images, achieving an accuracy of 92.3% and an mAP@.5 of 93.7%. The proposed MobileNet-CA-YOLO model is a high-performance and lightweight solution for rice pest and disease detection, providing accurate and timely results for farmers and researchers.

With the rapid advancement of plant phenotyping research, understanding plant genetic information and growth trends has become crucial. Measuring seedling length is a key criterion for assessing seed viability, but traditional ruler-based methods are time-consuming and labor-intensive. To address these limitations, we propose an efficient deep learning approach to enhance plant seedling phenotyping analysis. We improved the DeepLabv3+ model, naming it DFMA, and introduced a novel ASPP structure, PSPA-ASPP. On our self-constructed rice seedling dataset, the model achieved a mean Intersection over Union (mIoU) of 81.72%. On publicly available datasets, including Arabidopsis thaliana, Brachypodium distachyon, and Sinapis alba, detection scores reached 87.69%, 91.07%, and 66.44%, respectively, outperforming existing models. The model generates detailed segmentation masks, capturing structures such as the embryonic shoot, axis, and root, while a seedling length measurement algorithm provides precise parameters for component development. This approach offers a comprehensive, automated solution, improving phenotyping analysis efficiency and addressing the challenges of traditional methods.

To achieve a rapid and accurate estimation of the soil organic matter (SOM) content in wetland soil, we focused on surface soil samples from the Xianshan Lake wetland area in Zhejiang Province and proposed a novel method called Marine-Predators-Algorithm-Based Random Forest (MPARF) to establish a fast detection model for the SOM content. This study analyzed 85 soil samples from the study area with the aim of assessing the performance of various combinations of ten differential transformation methods and five regression algorithms in predicting the SOM content. Our research findings demonstrate that the combination of second-order differentiation (SD) and MPARF yields the best results, with the highest R² value (0.92) and the lowest RMSE (1.32 g/kg). Furthermore, we determined that the average SOM content in the study area’s soil is 9.77 g/kg. Additionally, we confirmed that different differential transformation methods contribute to improving the correlation between spectral data and the SOM content, thereby enhancing the development of predictive models. This study provides a robust methodology and foundation for future soil organic matter monitoring in the region.

Rice disease detection is vital for food security, prevention efficiency, pesticide reduction, and sustainable agriculture. Challenges like poor model appl

Hyperspectral imaging for seed vigor detection faces the challenges of handling high-dimensional spectral data, information loss after dimensionality reduction, and low feature differentiation between vigor levels. To address the above issues, this study proposes an improved dynamic optimize MDS (DO-MDS) dimensionality reduction algorithm based on multidimensional scaling transformation. DO-MDS better preserves key features between samples during dimensionality reduction. Secondly, a dual-stream spectral collaborative attention (DSCA) module is proposed. The DSCA module adopts a dual-modal fusion approach combining global feature capture and local feature enhancement, deepening the characterization capability of spectral features. This study selected commonly used rice seed varieties in Zhejiang Province and constructed three individual spectral datasets and a mixed dataset through aging, spectral acquisition, and germination experiments. The experiments involved using the DO-MDS processed datasets with a convolutional neural network embedded with the DSCA attention module, and the results demonstrate vigor discrimination accuracy rates of 93.85%, 93.4%, and 96.23% for the Chunyou 83, Zhongzao 39, and Zhongzu 53 datasets, respectively, achieving 94.8% for the mixed dataset. This study provides effective strategies for spectral dimensionality reduction in hyperspectral seed vigor detection and enhances the differentiation of spectral information for seeds with similar vigor levels.

Accurate and efficient tomato ripeness estimation is crucial for robotic harvesting and supply chain grading in smart agriculture. However, manual visual inspection is subjective, slow and difficult to scale, while existing vision models often struggle with cluttered field backgrounds, small targets and limited throughput. To overcome these limitations, we introduce TSE-YOLO, an improved real-time detector tailored for tomato ripeness estimation with joint detection and segmentation. In the TSE-YOLO model, three key enhancements are introduced. The C2PSA module is improved with ConvGLU, adapted from TransNeXt, to strengthen feature extraction within tomato regions. A novel segmentation head is designed to accelerate ripeness-aware segmentation and improve recall. Additionally, the C3k2 module is augmented with partial and frequency-dynamic convolutions, enhancing feature representation under complex planting conditions. These components enable precise instance-level localization and pixel-wise segmentation of tomatoes at three ripeness stages: verde, semi-ripe (semi-maduro), and ripe. Experiments on a self-constructed tomato ripeness dataset demonstrate that TSE-YOLO achieves 92.5% mAP@0.5 for detection and 92.2% mAP@0.5 for segmentation with only 9.8 GFLOPs. Deployed on Android via Ncnn Convolutional Neural Network (NCNN), the model runs at 30 fps on Dimensity 9300, offering a practical solution for automated tomato harvesting and grading that accelerates smart agriculture’s industrial adoption.